Intelligent tutoring systems help students acquire cognitive
skills by tracing students’ knowledge and providing relevant feedback. However, feedback that focuses only on the cognitive level might not be optimal - errors are often the result of inappropriate metacognitive decisions. We have developed two models which detect aspects of student faulty
metacognitive behavior: A prescriptive rational model aimed at improving help-seeking behavior, and a descriptive machine-learned model aimed at eliminating attempts to “game” the tutor. In a comparison between the two models we found that while both successfully identify gaming behavior, one
is better at characterizing the types of problems students game in, and the other captures a larger variety of faulty behaviors. An analysis of students' actions in two different tutors suggests that the help-seeking model is domain independent, and that students’ behavior is fairly consistent across classrooms, age groups, domains, and task elements.
%0 Journal Article
%1 roll2005msm
%A Roll, Ido
%A Baker, Ryan S.
%A Aleven, Vincent
%A McLaren, Bruce M.
%A Koedinger, Kenneth R.
%D 2005
%I Springer
%J LECTURE NOTES IN COMPUTER SCIENCE
%K ITS behaviour gamingthesystem learner learning metacognitive modelling wleformativeeassessment
%P 367
%T Modeling Students' Metacognitive Errors in Two Intelligent Tutoring Systems
%U http://www.psychology.nottingham.ac.uk/staff/lpzrsb/RollUM05.pdf
%V 3538
%X Intelligent tutoring systems help students acquire cognitive
skills by tracing students’ knowledge and providing relevant feedback. However, feedback that focuses only on the cognitive level might not be optimal - errors are often the result of inappropriate metacognitive decisions. We have developed two models which detect aspects of student faulty
metacognitive behavior: A prescriptive rational model aimed at improving help-seeking behavior, and a descriptive machine-learned model aimed at eliminating attempts to “game” the tutor. In a comparison between the two models we found that while both successfully identify gaming behavior, one
is better at characterizing the types of problems students game in, and the other captures a larger variety of faulty behaviors. An analysis of students' actions in two different tutors suggests that the help-seeking model is domain independent, and that students’ behavior is fairly consistent across classrooms, age groups, domains, and task elements.
@article{roll2005msm,
abstract = {Intelligent tutoring systems help students acquire cognitive
skills by tracing students’ knowledge and providing relevant feedback. However, feedback that focuses only on the cognitive level might not be optimal - errors are often the result of inappropriate metacognitive decisions. We have developed two models which detect aspects of student faulty
metacognitive behavior: A prescriptive rational model aimed at improving help-seeking behavior, and a descriptive machine-learned model aimed at eliminating attempts to “game” the tutor. In a comparison between the two models we found that while both successfully identify gaming behavior, one
is better at characterizing the types of problems students game in, and the other captures a larger variety of faulty behaviors. An analysis of students' actions in two different tutors suggests that the help-seeking model is domain independent, and that students’ behavior is fairly consistent across classrooms, age groups, domains, and task elements.},
added-at = {2008-09-17T02:38:17.000+0200},
author = {Roll, Ido and Baker, Ryan S. and Aleven, Vincent and McLaren, Bruce M. and Koedinger, Kenneth R.},
biburl = {https://www.bibsonomy.org/bibtex/24b97dd3dc26f97c6dbda6835e30835b2/yish},
interhash = {c31a905002ce65109ce355daaef309dd},
intrahash = {4b97dd3dc26f97c6dbda6835e30835b2},
journal = {LECTURE NOTES IN COMPUTER SCIENCE},
keywords = {ITS behaviour gamingthesystem learner learning metacognitive modelling wleformativeeassessment},
pages = 367,
publisher = {Springer},
timestamp = {2008-09-17T02:38:17.000+0200},
title = {Modeling Students' Metacognitive Errors in Two Intelligent Tutoring Systems},
url = {http://www.psychology.nottingham.ac.uk/staff/lpzrsb/RollUM05.pdf},
volume = 3538,
year = 2005
}